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IEEE Transactions Circuits and Systems-I</title> <meta name="description" content="Search results for: A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I"> <meta name="keywords" content="A projection neural network and its application to constrained optimization problems. 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IEEE Transactions Circuits and Systems-I"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 20870</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20870</span> Solving the Quadratic Programming Problem Using a Recurrent Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Behroozpoor">A. A. Behroozpoor</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Mazarei"> M. M. Mazarei </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a fuzzy recurrent neural network is proposed for solving the classical quadratic control problem subject to linear equality and bound constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=REFERENCES%20%20%0D%0A%5B1%5D%09Xia" title="REFERENCES [1] Xia">REFERENCES [1] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y" title=" Y"> Y</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20new%20neural%20network%20for%20solving%20linear%20and%20quadratic%20programming%20problems.%20IEEE%20Transactions%20on%20Neural%20Networks" title=" A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks"> A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=7%286%29" title=" 7(6)"> 7(6)</a>, <a href="https://publications.waset.org/abstracts/search?q=1996" title=" 1996"> 1996</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.1544%E2%80%931548.%0D%0A%5B2%5D%09Xia" title=" pp.1544–1548. [2] Xia"> pp.1544–1548. [2] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y." title=" Y."> Y.</a>, <a href="https://publications.waset.org/abstracts/search?q=%26%20Wang" title=" & Wang"> & Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=J" title=" J"> J</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20recurrent%20neural%20network%20for%20solving%20nonlinear%20convex%20programs%20subject%20to%20linear%20constraints.%20IEEE%20Transactions%20on%20Neural%20Networks" title=" A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks"> A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=16%282%29" title="16(2)">16(2)</a>, <a href="https://publications.waset.org/abstracts/search?q=2005" title=" 2005"> 2005</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.%20379%E2%80%93386.%0D%0A%5B3%5D%09Xia" title=" pp. 379–386. [3] Xia"> pp. 379–386. [3] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y." title=" Y."> Y.</a>, <a href="https://publications.waset.org/abstracts/search?q=H" title=" H"> H</a>, <a href="https://publications.waset.org/abstracts/search?q=Leung" title=" Leung"> Leung</a>, <a href="https://publications.waset.org/abstracts/search?q=%26%20J" title=" & J"> & J</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang" title=" Wang"> Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20projection%20neural%20network%20and%20its%20application%20to%20constrained%20optimization%20problems.%20IEEE%20Transactions%20Circuits%20and%20Systems-I" title=" A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I"> A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I</a>, <a href="https://publications.waset.org/abstracts/search?q=49%284%29" title=" 49(4)"> 49(4)</a>, <a href="https://publications.waset.org/abstracts/search?q=2002" title=" 2002"> 2002</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.447%E2%80%93458.B.%20%0D%0A%5B4%5D%09Q.%20Liu" title=" pp.447–458.B. [4] Q. Liu"> pp.447–458.B. [4] Q. Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Guo" title=" Z. Guo"> Z. Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Wang" title=" J. Wang"> J. Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20one-layer%20recurrent%20neural%20network%20for%20constrained%20seudoconvex%20optimization%20and%20its%20application%20for%20dynamic%20portfolio%20optimization.%20Neural%20Networks" title=" A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks"> A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=26" title=" 26"> 26</a>, <a href="https://publications.waset.org/abstracts/search?q=2012" title=" 2012"> 2012</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.%2099-109." title=" pp. 99-109. "> pp. 99-109. </a> </p> <a href="https://publications.waset.org/abstracts/19435/solving-the-quadratic-programming-problem-using-a-recurrent-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19435.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">643</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20869</span> A Constrained Neural Network Based Variable Neighborhood Search for the Multi-Objective Dynamic Flexible Job Shop Scheduling Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ozan%20Bahadir"> Ozan Bahadir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new neural network based variable neighborhood search is proposed for the multi-objective dynamic, flexible job shop scheduling problems. The neural network controls the problems' constraints to prevent infeasible solutions, while the Variable Neighborhood Search (VNS) applies moves, based on the critical block concept to improve the solutions. Two approaches are used for managing the constraints, in the first approach, infeasible solutions are modified according to the constraints, after the moves application, while in the second one, infeasible moves are prevented. Several neighborhood structures from the literature with some modifications, also new structures are used in the VNS. The suggested neighborhoods are more systematically defined and easy to implement. Comparison is done based on a multi-objective flexible job shop scheduling problem that is dynamic because of the jobs different release time and machines breakdowns. The results show that the presented method has better performance than the compared VNSs selected from the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=constrained%20optimization" title="constrained optimization">constrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20neighborhood%20search" title=" variable neighborhood search"> variable neighborhood search</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop%20scheduling" title=" flexible job shop scheduling"> flexible job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20multi-objective%20optimization" title=" dynamic multi-objective optimization"> dynamic multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/71919/a-constrained-neural-network-based-variable-neighborhood-search-for-the-multi-objective-dynamic-flexible-job-shop-scheduling-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71919.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">346</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20868</span> A Hybrid Hopfield Neural Network for Dynamic Flexible Job Shop Scheduling Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new hybrid Hopfield neural network is proposed for the dynamic, flexible job shop scheduling problem. A new heuristic based and easy to implement energy function is designed for the Hopfield neural network, which penalizes the constraints violation and decreases makespan. Moreover, for enhancing the performance, several heuristics are integrated to it that achieve active, and non-delay schedules also, prevent early convergence of the neural network. The suggested algorithm that is designed as a generalization of the previous studies for the flexible and dynamic scheduling problems can be used for solving real scheduling problems. Comparison of the presented hybrid method results with the previous studies results proves its efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20flexible%20job%20shop%20scheduling" title="dynamic flexible job shop scheduling">dynamic flexible job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=constrained%20optimization" title=" constrained optimization"> constrained optimization</a> </p> <a href="https://publications.waset.org/abstracts/72143/a-hybrid-hopfield-neural-network-for-dynamic-flexible-job-shop-scheduling-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72143.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20867</span> Cuckoo Search (CS) Optimization Algorithm for Solving Constrained Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sait%20Ali%20Uymaz">Sait Ali Uymaz</a>, <a href="https://publications.waset.org/abstracts/search?q=G%C3%BClay%20Tezel"> Gülay Tezel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the comparison results on the performance of the Cuckoo Search (CS) algorithm for constrained optimization problems. For constraint handling, CS algorithm uses penalty method. CS algorithm is tested on thirteen well-known test problems and the results obtained are compared to Particle Swarm Optimization (PSO) algorithm. Mean, best, median and worst values were employed for the analyses of performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cuckoo%20search" title="cuckoo search">cuckoo search</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=constrained%20optimization%20problems" title=" constrained optimization problems"> constrained optimization problems</a>, <a href="https://publications.waset.org/abstracts/search?q=penalty%20method" title=" penalty method"> penalty method</a> </p> <a href="https://publications.waset.org/abstracts/13991/cuckoo-search-cs-optimization-algorithm-for-solving-constrained-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13991.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">557</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20866</span> Optimization of Vertical Axis Wind Turbine Based on Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Affanuddin%20H.%20Siddique">Mohammed Affanuddin H. Siddique</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayesh%20S.%20Shukla"> Jayesh S. Shukla</a>, <a href="https://publications.waset.org/abstracts/search?q=Chetan%20B.%20Meshram"> Chetan B. Meshram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=VAWT" title="VAWT">VAWT</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20design" title=" inverse design"> inverse design</a> </p> <a href="https://publications.waset.org/abstracts/91997/optimization-of-vertical-axis-wind-turbine-based-on-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91997.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">323</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20865</span> On the Performance Analysis of Coexistence between IEEE 802.11g and IEEE 802.15.4 Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chompunut%20Jantarasorn">Chompunut Jantarasorn</a>, <a href="https://publications.waset.org/abstracts/search?q=Chutima%20Prommak"> Chutima Prommak </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an intensive measurement studying of the network performance analysis when IEEE 802.11g Wireless Local Area Networks (WLAN) coexisting with IEEE 802.15.4 Wireless Personal Area Network (WPAN). The measurement results show that the coexistence between both networks could increase the Frame Error Rate (FER) of the IEEE 802.15.4 networks up to 60% and it could decrease the throughputs of the IEEE 802.11g networks up to 55%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20performance%20analysis" title="wireless performance analysis">wireless performance analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=coexistence%20analysis" title=" coexistence analysis"> coexistence analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=IEEE%20802.11g" title=" IEEE 802.11g"> IEEE 802.11g</a>, <a href="https://publications.waset.org/abstracts/search?q=IEEE%20802.15.4" title=" IEEE 802.15.4"> IEEE 802.15.4</a> </p> <a href="https://publications.waset.org/abstracts/9834/on-the-performance-analysis-of-coexistence-between-ieee-80211g-and-ieee-802154-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9834.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">552</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20864</span> Optimisation of the Input Layer Structure for Feedforward Narx Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zongyan%20Li">Zongyan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Matt%20Best"> Matt Best</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation%20analysis" title="correlation analysis">correlation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=F-ratio" title=" F-ratio"> F-ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=levenberg-marquardt" title=" levenberg-marquardt"> levenberg-marquardt</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=NARX" title=" NARX"> NARX</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=optimisation" title=" optimisation"> optimisation</a> </p> <a href="https://publications.waset.org/abstracts/23195/optimisation-of-the-input-layer-structure-for-feedforward-narx-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23195.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">371</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20863</span> Profit-Based Artificial Neural Network (ANN) Trained by Migrating Birds Optimization: A Case Study in Credit Card Fraud Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashkan%20Zakaryazad">Ashkan Zakaryazad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ekrem%20Duman"> Ekrem Duman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=profit-based%20neural%20network" title=" profit-based neural network"> profit-based neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20of%20squared%20errors%20%28SSE%29" title=" sum of squared errors (SSE)"> sum of squared errors (SSE)</a>, <a href="https://publications.waset.org/abstracts/search?q=MBO" title=" MBO"> MBO</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20descent" title=" gradient descent"> gradient descent</a> </p> <a href="https://publications.waset.org/abstracts/31637/profit-based-artificial-neural-network-ann-trained-by-migrating-birds-optimization-a-case-study-in-credit-card-fraud-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31637.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">475</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20862</span> Solutions to Probabilistic Constrained Optimal Control Problems Using Concentration Inequalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, optimal control problems subject to probabilistic constraints have attracted much attention in many research field. Although probabilistic constraints are generally intractable in optimization problems, several methods haven been proposed to deal with probabilistic constraints. In most methods, probabilistic constraints are transformed to deterministic constraints that are tractable in optimization problems. This paper examines a method for transforming probabilistic constraints into deterministic constraints for a class of probabilistic constrained optimal control problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete-time%20systems" title=" discrete-time systems"> discrete-time systems</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20constraints" title=" probabilistic constraints"> probabilistic constraints</a> </p> <a href="https://publications.waset.org/abstracts/57973/solutions-to-probabilistic-constrained-optimal-control-problems-using-concentration-inequalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57973.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20861</span> Demand Forecasting Using Artificial Neural Networks Optimized by Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daham%20Owaid%20Matrood">Daham Owaid Matrood</a>, <a href="https://publications.waset.org/abstracts/search?q=Naqaa%20Hussein%20Raheem"> Naqaa Hussein Raheem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20forecasting" title=" demand forecasting"> demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=weight%20optimization" title=" weight optimization"> weight optimization</a> </p> <a href="https://publications.waset.org/abstracts/45069/demand-forecasting-using-artificial-neural-networks-optimized-by-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45069.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">451</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20860</span> Robotic Arm Control with Neural Networks Using Genetic Algorithm Optimization Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arbnor%20Pajaziti">Arbnor Pajaziti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Cana"> Hasan Cana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the structural genetic algorithm is used to optimize the neural network to control the joint movements of robotic arm. The robotic arm has also been modeled in 3D and simulated in real-time in MATLAB. It is found that Neural Networks provide a simple and effective way to control the robot tasks. Computer simulation examples are given to illustrate the significance of this method. By combining Genetic Algorithm optimization method and Neural Networks for the given robotic arm with 5 D.O.F. the obtained the results shown that the base joint movements overshooting time without controller was about 0.5 seconds, while with Neural Network controller (optimized with Genetic Algorithm) was about 0.2 seconds, and the population size of 150 gave best results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robotic%20arm" title="robotic arm">robotic arm</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/7408/robotic-arm-control-with-neural-networks-using-genetic-algorithm-optimization-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7408.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">523</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20859</span> A Multi-Objective Evolutionary Algorithm of Neural Network for Medical Diseases Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sultan%20Noman%20Qasem">Sultan Noman Qasem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20network" title="radial basis function network">radial basis function network</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20learning" title=" hybrid learning"> hybrid learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/15843/a-multi-objective-evolutionary-algorithm-of-neural-network-for-medical-diseases-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15843.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">563</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20858</span> Forecasting Optimal Production Program Using Profitability Optimization by Genetic Algorithm and Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Galal%20H.%20Senussi">Galal H. Senussi</a>, <a href="https://publications.waset.org/abstracts/search?q=Muamar%20Benisa"> Muamar Benisa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanja%20Vasin"> Sanja Vasin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network. The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters. Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output. This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc. From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=project%20profitability" title="project profitability">project profitability</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20set" title=" Pareto set"> Pareto set</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/9868/forecasting-optimal-production-program-using-profitability-optimization-by-genetic-algorithm-and-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9868.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20857</span> Optimization of Structures Subjected to Earthquake</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20%20Lavaei">Alireza Lavaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20%20Lohrasbi"> Alireza Lohrasbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadali%20M.%20Shahlaei"> Mohammadali M. Shahlaei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To reduce the overall time of structural optimization for earthquake loads two strategies are adopted. In the first strategy, a neural system consisting self-organizing map and radial basis function neural networks, is utilized to predict the time history responses. In this case, the input space is classified by employing a self-organizing map neural network. Then a distinct RBF neural network is trained in each class. In the second strategy, an improved genetic algorithm is employed to find the optimum design. A 72-bar space truss is designed for optimal weight using exact and approximate analysis for the El Centro (S-E 1940) earthquake loading. The numerical results demonstrate the computational advantages and effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a> </p> <a href="https://publications.waset.org/abstracts/53234/optimization-of-structures-subjected-to-earthquake" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53234.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">311</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20856</span> Analysis of the IEEE 802.15.4 MAC Parameters to Achive Lower Packet Loss Rates</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imen%20Bouazzi">Imen Bouazzi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The IEEE-802.15.4 standard utilizes the CSMA-CA mechanism to control nodes access to the shared wireless communication medium. It is becoming the popular choice for various applications of surveillance and control used in wireless sensor network (WSN). The benefit of this standard is evaluated regarding of the packet loss probability who depends on the configuration of IEEE 802.15.4 MAC parameters and the traffic load. Our exigency is to evaluate the effects of various configurable MAC parameters on the performance of beaconless IEEE 802.15.4 networks under different traffic loads, static values of IEEE 802.15.4 MAC parameters (macMinBE, macMaxCSMABackoffs, and macMaxFrame Retries) will be evaluated. To performance analysis, we use ns-2[2] network simulator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=WSN" title="WSN">WSN</a>, <a href="https://publications.waset.org/abstracts/search?q=packet%20loss" title=" packet loss"> packet loss</a>, <a href="https://publications.waset.org/abstracts/search?q=CSMA%2FCA" title=" CSMA/CA"> CSMA/CA</a>, <a href="https://publications.waset.org/abstracts/search?q=IEEE-802.15.4" title=" IEEE-802.15.4 "> IEEE-802.15.4 </a> </p> <a href="https://publications.waset.org/abstracts/21701/analysis-of-the-ieee-802154-mac-parameters-to-achive-lower-packet-loss-rates" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21701.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">340</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20855</span> Conservativeness of Probabilistic Constrained Optimal Control Method for Unknown Probability Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent decades, probabilistic constrained optimal control problems have attracted much attention in many research field. Although probabilistic constraints are generally intractable in an optimization problem, several tractable methods haven been proposed to handle probabilistic constraints. In most methods, probabilistic constraints are reduced to deterministic constraints that are tractable in an optimization problem. However, there is a gap between the transformed deterministic constraints in case of known and unknown probability distribution. This paper examines the conservativeness of probabilistic constrained optimization method with the unknown probability distribution. The objective of this paper is to provide a quantitative assessment of the conservatism for tractable constraints in probabilistic constrained optimization with the unknown probability distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20time%20systems" title=" discrete time systems"> discrete time systems</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20constraints" title=" probabilistic constraints"> probabilistic constraints</a> </p> <a href="https://publications.waset.org/abstracts/35585/conservativeness-of-probabilistic-constrained-optimal-control-method-for-unknown-probability-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35585.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">580</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20854</span> Design an Intelligent Fire Detection System Based on Neural Network and Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Arvan">Majid Arvan</a>, <a href="https://publications.waset.org/abstracts/search?q=Peyman%20Beygi"> Peyman Beygi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sina%20Rokhsati"> Sina Rokhsati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20fire%20detection" title="intelligent fire detection">intelligent fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=fire%20sensor%20network" title=" fire sensor network"> fire sensor network</a> </p> <a href="https://publications.waset.org/abstracts/55735/design-an-intelligent-fire-detection-system-based-on-neural-network-and-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55735.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">380</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20853</span> Robot Movement Using the Trust Region Policy Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Romisaa%20Ali">Romisaa Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title="deep neural networks">deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=proximal%20policy%20optimization" title=" proximal policy optimization"> proximal policy optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=state-of-the-art" title=" state-of-the-art"> state-of-the-art</a>, <a href="https://publications.waset.org/abstracts/search?q=trust%20region%20policy%20optimization" title=" trust region policy optimization"> trust region policy optimization</a> </p> <a href="https://publications.waset.org/abstracts/158075/robot-movement-using-the-trust-region-policy-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158075.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">169</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20852</span> A Survey of Field Programmable Gate Array-Based Convolutional Neural Network Accelerators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei%20Zhang">Wei Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20programmable%20gate%20array" title=" field programmable gate array"> field programmable gate array</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=hardware%20accelerator" title=" hardware accelerator"> hardware accelerator</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a> </p> <a href="https://publications.waset.org/abstracts/128017/a-survey-of-field-programmable-gate-array-based-convolutional-neural-network-accelerators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">128</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20851</span> Second Order Optimality Conditions in Nonsmooth Analysis on Riemannian Manifolds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyedehsomayeh%20Hosseini">Seyedehsomayeh Hosseini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Much attention has been paid over centuries to understanding and solving the problem of minimization of functions. Compared to linear programming and nonlinear unconstrained optimization problems, nonlinear constrained optimization problems are much more difficult. Since the procedure of finding an optimizer is a search based on the local information of the constraints and the objective function, it is very important to develop techniques using geometric properties of the constraints and the objective function. In fact, differential geometry provides a powerful tool to characterize and analyze these geometric properties. Thus, there is clearly a link between the techniques of optimization on manifolds and standard constrained optimization approaches. Furthermore, there are manifolds that are not defined as constrained sets in R^n an important example is the Grassmann manifolds. Hence, to solve optimization problems on these spaces, intrinsic methods are used. In a nondifferentiable problem, the gradient information of the objective function generally cannot be used to determine the direction in which the function is decreasing. Therefore, techniques of nonsmooth analysis are needed to deal with such a problem. As a manifold, in general, does not have a linear structure, the usual techniques, which are often used in nonsmooth analysis on linear spaces, cannot be applied and new techniques need to be developed. This paper presents necessary and sufficient conditions for a strict local minimum of extended real-valued, nonsmooth functions defined on Riemannian manifolds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Riemannian%20manifolds" title="Riemannian manifolds">Riemannian manifolds</a>, <a href="https://publications.waset.org/abstracts/search?q=nonsmooth%20optimization" title=" nonsmooth optimization"> nonsmooth optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=lower%20semicontinuous%20functions" title=" lower semicontinuous functions"> lower semicontinuous functions</a>, <a href="https://publications.waset.org/abstracts/search?q=subdifferential" title=" subdifferential"> subdifferential</a> </p> <a href="https://publications.waset.org/abstracts/35809/second-order-optimality-conditions-in-nonsmooth-analysis-on-riemannian-manifolds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35809.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">361</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20850</span> Optimizing the Probabilistic Neural Network Training Algorithm for Multi-Class Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelhadi%20Lotfi">Abdelhadi Lotfi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Benyettou"> Abdelkader Benyettou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20neural%20networks" title=" probabilistic neural networks"> probabilistic neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20optimization" title=" network optimization"> network optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/104139/optimizing-the-probabilistic-neural-network-training-algorithm-for-multi-class-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104139.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">262</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20849</span> A Survey of Sentiment Analysis Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pingping%20Lin">Pingping Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yifan%20Fan"> Yifan Fan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20analysis" title="document analysis">document analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20sentiment%20analysis" title=" multimodal sentiment analysis"> multimodal sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/130107/a-survey-of-sentiment-analysis-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130107.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">164</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20848</span> Optimization of Assay Parameters of L-Glutaminase from Bacillus cereus MTCC1305 Using Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Singh">P. Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Banik"> R. M. Banik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural network (ANN) was employed to optimize assay parameters viz., time, temperature, pH of reaction mixture, enzyme volume and substrate concentration of L-glutaminase from Bacillus cereus MTCC 1305. ANN model showed high value of coefficient of determination (0.9999), low value of root mean square error (0.6697) and low value of absolute average deviation. A multilayer perceptron neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model and its topology was obtained as 5-3-1 after applying Levenberg Marquardt (LM) training algorithm. The predicted activity of L-glutaminase was obtained as 633.7349 U/l by considering optimum assay parameters, viz., pH of reaction mixture (7.5), reaction time (20 minutes), incubation temperature (35˚C), substrate concentration (40mM), and enzyme volume (0.5ml). The predicted data was verified by running experiment at simulated optimum assay condition and activity was obtained as 634.00 U/l. The application of ANN model for optimization of assay conditions improved the activity of L-glutaminase by 1.499 fold. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bacillus%20cereus" title="Bacillus cereus">Bacillus cereus</a>, <a href="https://publications.waset.org/abstracts/search?q=L-glutaminase" title=" L-glutaminase"> L-glutaminase</a>, <a href="https://publications.waset.org/abstracts/search?q=assay%20parameters" title=" assay parameters"> assay parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a> </p> <a href="https://publications.waset.org/abstracts/13443/optimization-of-assay-parameters-of-l-glutaminase-from-bacillus-cereus-mtcc1305-using-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13443.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">429</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20847</span> The Intersection of Artificial Intelligence and Mathematics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mitat%20Uysal">Mitat Uysal</a>, <a href="https://publications.waset.org/abstracts/search?q=Aynur%20Uysal"> Aynur Uysal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial Intelligence (AI) is fundamentally driven by mathematics, with many of its core algorithms rooted in mathematical principles such as linear algebra, probability theory, calculus, and optimization techniques. This paper explores the deep connection between AI and mathematics, highlighting the role of mathematical concepts in key AI techniques like machine learning, neural networks, and optimization. To demonstrate this connection, a case study involving the implementation of a neural network using Python is presented. This practical example illustrates the essential role that mathematics plays in training a model and solving real-world problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AI" title="AI">AI</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematics" title=" mathematics"> mathematics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20techniques" title=" optimization techniques"> optimization techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/193104/the-intersection-of-artificial-intelligence-and-mathematics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193104.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">14</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20846</span> The Application of a Hybrid Neural Network for Recognition of a Handwritten Kazakh Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Almagul%20%20Assainova">Almagul Assainova </a>, <a href="https://publications.waset.org/abstracts/search?q=Dariya%20Abykenova"> Dariya Abykenova</a>, <a href="https://publications.waset.org/abstracts/search?q=Liudmila%20Goncharenko"> Liudmila Goncharenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20%20Sybachin"> Sergey Sybachin</a>, <a href="https://publications.waset.org/abstracts/search?q=Saule%20Rakhimova"> Saule Rakhimova</a>, <a href="https://publications.waset.org/abstracts/search?q=Abay%20Aman"> Abay Aman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The recognition of a handwritten Kazakh text is a relevant objective today for the digitization of materials. The study presents a model of a hybrid neural network for handwriting recognition, which includes a convolutional neural network and a multi-layer perceptron. Each network includes 1024 input neurons and 42 output neurons. The model is implemented in the program, written in the Python programming language using the EMNIST database, NumPy, Keras, and Tensorflow modules. The neural network training of such specific letters of the Kazakh alphabet as ә, ғ, қ, ң, ө, ұ, ү, h, і was conducted. The neural network model and the program created on its basis can be used in electronic document management systems to digitize the Kazakh text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=handwriting%20recognition%20system" title="handwriting recognition system">handwriting recognition system</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20recognition" title=" image recognition"> image recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazakh%20font" title=" Kazakh font"> Kazakh font</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/129773/the-application-of-a-hybrid-neural-network-for-recognition-of-a-handwritten-kazakh-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129773.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">262</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20845</span> Optimization of Agricultural Water Demand Using a Hybrid Model of Dynamic Programming and Neural Networks: A Case Study of Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Boudjerda">M. Boudjerda</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Touaibia"> B. Touaibia</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20K.%20Mihoubi"> M. K. Mihoubi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Algeria agricultural irrigation is the primary water consuming sector followed by the domestic and industrial sectors. Economic development in the last decade has weighed heavily on water resources which are relatively limited and gradually decreasing to the detriment of agriculture. The research presented in this paper focuses on the optimization of irrigation water demand. Dynamic Programming-Neural Network (DPNN) method is applied to investigate reservoir optimization. The optimal operation rule is formulated to minimize the gap between water release and water irrigation demand. As a case study, Foum El-Gherza dam’s reservoir system in south of Algeria has been selected to examine our proposed optimization model. The application of DPNN method allowed increasing the satisfaction rate (SR) from 12.32% to 55%. In addition, the operation rule generated showed more reliable and resilience operation for the examined case study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=water%20management" title="water management">water management</a>, <a href="https://publications.waset.org/abstracts/search?q=agricultural%20demand" title=" agricultural demand"> agricultural demand</a>, <a href="https://publications.waset.org/abstracts/search?q=dam%20and%20reservoir%20operation" title=" dam and reservoir operation"> dam and reservoir operation</a>, <a href="https://publications.waset.org/abstracts/search?q=Foum%20el-Gherza%20dam" title=" Foum el-Gherza dam"> Foum el-Gherza dam</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20programming" title=" dynamic programming"> dynamic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a> </p> <a href="https://publications.waset.org/abstracts/117003/optimization-of-agricultural-water-demand-using-a-hybrid-model-of-dynamic-programming-and-neural-networks-a-case-study-of-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/117003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">115</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20844</span> Artificial Neural Network-Based Short-Term Load Forecasting for Mymensingh Area of Bangladesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Anowarul%20Haque">S. M. Anowarul Haque</a>, <a href="https://publications.waset.org/abstracts/search?q=Md.%20Asiful%20Islam"> Md. Asiful Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=load%20forecasting" title="load forecasting">load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/133841/artificial-neural-network-based-short-term-load-forecasting-for-mymensingh-area-of-bangladesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133841.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">171</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20843</span> A Hybrid Feature Selection Algorithm with Neural Network for Software Fault Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalaf%20Khatatneh">Khalaf Khatatneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Nabeel%20Al-Milli"> Nabeel Al-Milli</a>, <a href="https://publications.waset.org/abstracts/search?q=Amjad%20Hudaib"> Amjad Hudaib</a>, <a href="https://publications.waset.org/abstracts/search?q=Monther%20Ali%20Tarawneh"> Monther Ali Tarawneh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20fault%20prediction" title=" software fault prediction"> software fault prediction</a> </p> <a href="https://publications.waset.org/abstracts/167733/a-hybrid-feature-selection-algorithm-with-neural-network-for-software-fault-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167733.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20842</span> Application of Adaptive Neural Network Algorithms for Determination of Salt Composition of Waters Using Laser Spectroscopy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tatiana%20A.%20Dolenko">Tatiana A. Dolenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20A.%20Burikov"> Sergey A. Burikov</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20O.%20Efitorov"> Alexander O. Efitorov</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20A.%20Dolenko"> Sergey A. Dolenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a comparative analysis of the approaches associated with the use of neural network algorithms for effective solution of a complex inverse problem – the problem of identifying and determining the individual concentrations of inorganic salts in multicomponent aqueous solutions by the spectra of Raman scattering of light – is performed. It is shown that application of artificial neural networks provides the average accuracy of determination of concentration of each salt no worse than 0.025 M. The results of comparative analysis of input data compression methods are presented. It is demonstrated that use of uniform aggregation of input features allows decreasing the error of determination of individual concentrations of components by 16-18% on the average. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inverse%20problems" title="inverse problems">inverse problems</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-component%20solutions" title=" multi-component solutions"> multi-component solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=Raman%20spectroscopy" title=" Raman spectroscopy"> Raman spectroscopy</a> </p> <a href="https://publications.waset.org/abstracts/14503/application-of-adaptive-neural-network-algorithms-for-determination-of-salt-composition-of-waters-using-laser-spectroscopy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14503.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">528</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20841</span> Adapting the Chemical Reaction Optimization Algorithm to the Printed Circuit Board Drilling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taisir%20Eldos">Taisir Eldos</a>, <a href="https://publications.waset.org/abstracts/search?q=Aws%20Kanan"> Aws Kanan</a>, <a href="https://publications.waset.org/abstracts/search?q=Waleed%20Nazih"> Waleed Nazih</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Khatatbih"> Ahmad Khatatbih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chemical Reaction Optimization (CRO) is an optimization metaheuristic inspired by the nature of chemical reactions as a natural process of transforming the substances from unstable to stable states. Starting with some unstable molecules with excessive energy, a sequence of interactions takes the set to a state of minimum energy. Researchers reported successful application of the algorithm in solving some engineering problems, like the quadratic assignment problem, with superior performance when compared with other optimization algorithms. We adapted this optimization algorithm to the Printed Circuit Board Drilling Problem (PCBDP) towards reducing the drilling time and hence improving the PCB manufacturing throughput. Although the PCBDP can be viewed as instance of the popular Traveling Salesman Problem (TSP), it has some characteristics that would require special attention to the transactions that explore the solution landscape. Experimental test results using the standard CROToolBox are not promising for practically sized problems, while it could find optimal solutions for artificial problems and small benchmarks as a proof of concept. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title="evolutionary algorithms">evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=chemical%20reaction%20optimization" title=" chemical reaction optimization"> chemical reaction optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman" title=" traveling salesman"> traveling salesman</a>, <a href="https://publications.waset.org/abstracts/search?q=board%20drilling" title=" board drilling"> board drilling</a> </p> <a href="https://publications.waset.org/abstracts/20797/adapting-the-chemical-reaction-optimization-algorithm-to-the-printed-circuit-board-drilling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20797.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">519</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=A%20projection%20neural%20network%20and%20its%20application%20to%20constrained%20optimization%20problems.%20IEEE%20Transactions%20Circuits%20and%20Systems-I&page=2">2</a></li> <li class="page-item"><a class="page-link" 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